aryyanthakrr/Kepler-Reasoning-7B
Kepler Reasoning 7B is a 7.6 billion parameter model developed by aryyanthakrr, created by merging Qwen2.5-Coder-7B-Instruct and Qwen2.5-Math-7B-Instruct using the SLERP method. This model is specifically optimized for strong local coding and mathematical reasoning tasks. It features a 32768 token context length, making it suitable for complex problem-solving in these domains.
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Kepler Reasoning 7B: Merged for Code and Math
Kepler Reasoning 7B is a 7.6 billion parameter language model developed by aryyanthakrr, specifically engineered for enhanced local performance in coding and mathematical reasoning. It was created by merging two specialized Qwen models: Qwen2.5-Coder-7B-Instruct and Qwen2.5-Math-7B-Instruct, utilizing the SLERP (Spherical Linear Interpolation) merge method.
Key Capabilities
- Coding Assistance: Provides support for various coding tasks.
- Python Problem Solving: Excels at solving problems specifically within the Python programming language.
- Mathematical Reasoning: Capable of handling complex mathematical problems.
- Algebra and Word Problems: Designed to address both algebraic equations and text-based math challenges.
- Local Inference: Optimized for efficient local deployment, including GGUF quantization.
Intended Use Cases
This model is ideal for developers and researchers requiring a robust local solution for:
- Generating and debugging code snippets.
- Solving programming challenges, particularly in Python.
- Assisting with mathematical computations and logical deductions.
- Tackling algebra and word problems in educational or technical contexts.
Limitations
As a 7B parameter model, Kepler Reasoning 7B is designed for local inference and specialized tasks. While highly capable in its target domains, it is not expected to outperform larger, frontier cloud-based models in general-purpose AI benchmarks.